Metadata-Version: 2.1
Name: pysad
Version: 0.1.1
Summary: PySAD is an open-source python framework for anomaly detection on streaming multivariate data. 
Home-page: https://github.com/selimfirat/pysad
Author: Selim Firat Yilmaz
Author-email: yilmazselimfirat@gmail.com
License: 3-Clause BSD
Download-URL: https://github.com/selimfirat/pysad/archive/master.zip
Description: .. image:: docs/logo.png
            :align: center
        
        Python Streaming Anomaly Detection (PySAD)
        ==========================================
        
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        **PySAD** is an open-source python framework for anomaly detection on streaming multivariate data.
        
        Features
        ========
        
        Online Anomaly Detection
        ^^^^^^^^^^^^^^^^^^^^^^^^
        
        `PySAD` provides methods for online/sequential anomaly detection, i.e. anomaly detection on streaming data, where model updates itself as a new instance arrives.
        
        
        Resource-Efficient
        ^^^^^^^^^^^^^^^^^^
        
        Streaming methods efficiently handle the limitied memory and processing time requirements of the data streams so that they can be used in near real-time. The methods can only store an instance or a small window of recent instances.
        
        
        Streaming Anomaly Detection Tools
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        `PySAD` contains stream simulators, evaluators, preprocessors, statistic trackers, postprocessors, probability calibrators and more.
        In addition to streaming models, `PySAD` also provides integrations for batch anomaly detectors of the `PyOD framework <https://github.com/yzhao062/pyod/>`_ so that they can be used in the streaming setting.
        
        
        Comprehensiveness
        ^^^^^^^^^^^^^^^^^
        
        `PySAD` provides models that are specifically designed for multivariate and univariate data. One can experiment via `PySAD` in supervised, semi-supervised and unsupervised setting.
        
        
        User Friendly
        ^^^^^^^^^^^^^
        
        Users with any experience level can easily use `PySAD`. One can easily design experiments and combine the tools in the framework. Moreover, the existing methods in `PySAD` are easy to extend.
        
        
        Free and Open Source Software (FOSS)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        `PySAD` is distributed under `3-Clause BSD License` and favors FOSS principles.
        
        Installation
        ============
        
        
        The PySAD framework can be installed via:
        
        
        .. code-block:: bash
        
            pip install -U pysad
        
        
        Alternatively, you can install the library directly using the source code in Github repository by:
        
        
        .. code-block:: bash
        
            git clone https://github.com/selimfirat/pysad.git
            cd pysad
            pip install .
        
        
        **Required Dependencies:**
        
        
        * numpy>=1.18.5
        * scipy>=1.4.1
        * scikit-learn>=0.23.2
        * pyod>=0.7.7.1
        
        **Optional Dependencies:**
        
        
        * rrcf==0.4.3 (Only required for  `pysad.models.robust_random_cut_forest.RobustRandomCutForest`)
        * PyNomaly==0.3.3 (Only required for  `pysad.models.loop.StreamLocalOutlierProbability`)
        * mmh3==2.5.1 (Only required for  `pysad.models.xstream.xStream`)
        * pandas==1.1.0 (Only required for  `pysad.utils.pandas_streamer.PandasStreamer`)
        
        Quick Links
        ============
        
        * `Github Repository <https://github.com/selimfirat/pysad/>`_
        
        * `Documentation <http://pysad.readthedocs.io/>`_
        
        * `PyPI Package <https://pypi.org/project/pysad>`_
        
        * `Travis CI <https://travis-ci.com/github/selimfirat/pysad>`_
        
        * `Azure Pipelines <https://dev.azure.com/selimfirat/pysad/>`_
        
        * `Circle CI <https://circleci.com/gh/selimfirat/pysad/>`_
        
        * `Appveyor <https://ci.appveyor.com/project/selimfirat/pysad/branch/master>`_
        
        * `Coveralls <https://coveralls.io/github/selimfirat/pysad?branch=master>`_
        
        * `License <https://github.com/selimfirat/pysad/blob/master/LICENSE>`_
        
        
        
        Versioning
        ==========
        
        `Semantic versioning <http://semver.org/>`_ is used for this project.
        
        License
        =======
        
        This project is licensed under the `3-Clause BSD License <LICENSE>`_.
        
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
